1. Amazon Access Samples: Amazon's InfoSec is getting smarter about the way Access data is leveraged. This is an anonymized sample of access provisioned within the company. 2. Bank Marketing: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y). 3. Blood Transfusion Service Center: Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan -- this is a classification problem. 4. CNAE-9: This is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a
subset of 9 categories 5. Credit Approval: This data concerns credit card applications; good mix of attributes 6. Economic Sanctions: Domain Theory on Economic Sanctions; Undocumented 7. Farm Ads: This data was collected from text ads found on twelve websites that deal with various farm animal related topics. The binary labels are based on whether or not the content owner approves of the ad. 8. Japanese Credit Screening: Includes domain theory (generated by talking to Japanese domain experts); data in Lisp 9. Reuters Transcribed Subset: This dataset is created by reading out 200 files from the 10 largest Reuters
classes and using an Automatic Speech Recognition system to create
corresponding transcriptions. 10. Statlog (Australian Credit Approval): This file concerns credit card applications. This database exists elsewhere in the repository (Credit Screening Database) in a slightly different form 11. Statlog (German Credit Data): This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix 12. Wine Quality: Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/). | 
